One important and essential technology in the feedback system using images or the visual support for autonomous moving robots is a technique for recognizing the motion information of an object within the environment. By such motion recognition technique, it becomes possible to detect the posture or the motion amount of the system or robot relative to a still object.
In some conventional motion recognition techniques that utilize two-dimensional images, the object is recognized from two-dimensional images which have been taken in time series and the direction and/or the posture of the object is judged to detect the speed of the object. Those conventional recognition techniques can be broadly classified into method based on comparison with geometrical models and method based on looking up images.
In the first method, the geometric model of the object is pre-stored and the structural analysis is performed on the obtained images of the object. Then the image is compared to the stored geometric model to recognize the object. In this method, it is difficult to exactly model the object for the complicated shape of the object. Another problem is that the complication of the geometric model increases the computing cost significantly.
On the other hand, the second method has an advantage that the computing cost is not dependent on the complicated shape of the object because images of the object in the various states is pre-stored and stored images are compared to the taken images. However, since in the method a large amount of various images are need to pre-stored as templates for the image lookup, a large amount of the storage capacity is required.
Many approaches for resolving these problems have been developed. One of those approaches is a technique in which the image lookup is performed by utilizing principal component analysis (PCA). Using the PCA, a plurality of variables that are correlated each other may be combined (or compressed) to generate a smaller number of the variables. Thus, in the image lookup using the PCA, various images to be used for the lookup may be compressed into eigenvectors, reducing the data amount to be stored.
An example of the conventional techniques using such PCA are disclosed in the article by Murase et al, entitled “3D Object Recognition from Appearance-Parametric Eigenspace Method”, Electro Information Communication Association D-II, Vol. J77-D-II, No. 11, pp 2179-2187, 1994/9. According to the above-referenced article, it is possible to store a 3-dimensional object as a set of 2-dimensional images in a less volume of the storage capacity using the PCA.
Other object recognition techniques using the similar eigenspace are disclosed in the article of Amano et al, titled “Retrieving Extracting the posture of an object from the distance image based on the eigenspace lookup-virtual distant image generation using shape models”, Image Lab Vol. 9, No, 1, 10-14, and in the Japanese Patent Publication No. H11-25269 by Kataoka et al, entitled “Apparatus and method for recognizing a face image”.
The aforementioned techniques using the PCA, however, are intended to recognize the direction or the posture of the object from taken images but not intended to recognize the motion information (motion direction and speed, for example) of the object directly by using the eigenspace. Thus, to recognize the motion information of the object in the aforementioned techniques, the direction or the posture must be recognized for each image and then the motion information of the object must be judged based on the recognition result. With such techniques the complication as well as the cost of computing tend to increase.
Therefore, it is objective of the invention to provide a high-speed and simple motion information recognition system that can reduce the required storage capacity and computing cost by obtaining the motion information directly from the images.